It may be challenging to automate the analysis and the processing of data that have been extracted from various documents and combined into one or more data reports. The structure of the underlying documents and the data reports might change, raw data are mixed with elements of the presentation layer of the data reports, and the labels are often not accurate enough to precisely identify the piece of information being disclosed. Users may need to manually prepare documents by mapping named ranges or adding unique identifiers, or building a hierarchy of elements including levels and weights. Those manual adaptations might significantly slow down the data processing workflow and cause errors.
For instance, people responsible for the design of companies' annual reports may not have direct access to the source information systems containing the underlying data. Moreover, the data structure might have been updated for publishing and might not be apparent from the raw numbers populating the report. It is a manually intensive process to review the report for data consistency and to remove data inconsistencies such as rounding errors from the financial-type documents.